It is widely known that a quick disclosure of the COVID-19 can help to reduce its spread dramatically. Transcriptase polymerase chain reaction could be a more useful, rapid, and trustworthy technique for the evaluation and classification of the COVID-19 disease. Currently, a computerized method for classifying computed tomography (CT) images of chests can be crucial for speeding up the detection while the COVID-19 epidemic is rapidly spreading. In this article, the authors have proposed an optimized convolutional neural network model (ADECO-CNN) to divide infected and not infected patients. Furthermore, the ADECO-CNN approach is compared with pretrained convolutional neural network (CNN)-based VGG19, GoogleNet, and ResNet models. Extensive analysis proved that the ADECO-CNN-optimized CNN model can classify CT images with 99.99% accuracy, 99.96% sensitivity, 99.92% precision, and 99.97% specificity.

中文翻译：

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COVID-19：使用优化的卷积神经网络从 CT 图像中自动检测新型冠状病毒疾病

众所周知，快速披露 COVID-19 有助于大幅减少其传播。转录酶聚合酶链反应可能是一种更有用、更快速、更值得信赖的 COVID-19 疾病评估和分类技术。目前，在 COVID-19 疫情迅速蔓延的情况下，对胸部计算机断层扫描 (CT) 图像进行分类的计算机化方法对于加快检测速度至关重要。在本文中，作者提出了一种优化的卷积神经网络模型（ADECO-CNN）来划分感染和未感染的患者。此外，还将 ADECO-CNN 方法与基于预训练的卷积神经网络 (CNN) 的 VGG19、GoogleNet 和 ResNet 模型进行了比较。大量分析证明，经过 ADECO-CNN 优化的 CNN 模型能够以 99.99% 的准确率、99.96% 的灵敏度、99.92% 的精确度和 99.97% 的特异性对 CT 图像进行分类。